1 Introduction
Let \(X_{i}\ ( i\geq1 )\) and X be real-valued random variables (r.v.s) with distributions \(F_{i}\ ( i\geq1 )\) and F and finite means \(\mu_{i} \ ( i\geq1 )\) and μ, respectively. Let \(S_{n}=\sum_{i=1}^{n}X_{i}\), \(n\geq1\), be the partial sums. This paper investigates the precise large deviations for these partial sums \(S_{n}\), \(n\geq1\). That is to say, the paper studies the asymptotics of \(P(S_{n}-E(S_{n})>x)\), which holds uniformly for all \(x\geq\gamma n\) for every fixed \(\gamma>0\) as n tends to ∞. In order to give the main results of this paper, we will introduce some notions and notation.
For a proper distribution V on \((-\infty,\infty)\), let \({\overline{V}}=1-V\) be its tail. Throughout this paper, all limit relations without explicit limit procedure are with respect to \(n\rightarrow \infty\). For two positive functions \(a(x)\) and \(b(x)\), we write \(a(x)=o(b(x))\) if \(\lim_{x\to\infty} a(x)/b(x)=0\) and write \(a(x)=O(b(x))\) if \(\limsup_{x\to\infty} a(x)/b(x)<\infty\). \({\mathbf {1}}_{A}\) is the indicator function of the event A. For a real-valued number c, let \(c^{+}=\max\{0, c\}\) and \(c^{-}=-\min\{0,c\}\).
In this paper, we consider the random variables with heavy-tailed distributions. Some subclasses of heavy-tailed distribution classes will be introduced in the following. If for all
\(\beta>0\),
$$\int_{-\infty}^{\infty} e^{\beta x}V(d x)=\infty, $$
we say that the r.v.
ξ (or its corresponding distribution
V) is heavy-tailed; otherwise, the r.v.
ξ (or
V) is called light-tailed. A subclass of heavy-tailed distribution class is the class
\(\mathscr {D}\), which consists of all distributions with dominantly varying tails. Say that a distribution
V on
\((-\infty,\infty)\) belongs to the class
\(\mathscr{D}\) if, for any
\(y\in(0,1)\),
$$\limsup_{x\to\infty}\frac{{\overline{V}}(xy)}{{\overline{V}}(x)}< \infty. $$
Another slightly smaller class is the class
\(\mathscr{C}\), which consists of all distributions with consistently varying tails. We say that a distribution
V on
\((-\infty,\infty)\) belongs to the class
\(\mathscr{C}\) if
$$\lim_{y\nearrow1}\limsup_{x\to\infty}\frac{{\overline{V}}(xy)}{{\overline{V}}(x)}=1,\quad \mbox{or, equivalently,}\quad \lim_{y\searrow1}\liminf_{x\to\infty} \frac{{\overline{V}}(xy)}{{\overline{V}}(x)}=1. $$
A subclass of the class
\(\mathscr{C}\) is the class of distributions with regularly varying tails. A distribution
V on
\((-\infty,\infty )\) is said to be regularly varying at infinity with index
α, denoted by
\(V\in\mathscr{R}_{-\alpha}\), if
$$\lim_{x\to\infty}\frac{\overline{V}(xy)}{\overline {V}(x)}=y^{-\alpha} $$
holds for some
\(0\leq\alpha<\infty\) and all
\(y>0\) (see, e.g., Bingham et al. [
1]).
For a distribution
V, denote the upper Matuszewska index of
V by
$$\begin{aligned} &J^{+}_{V}=-\lim_{y\to\infty}\frac{\log{\overline{V}}_{*}(y)}{\log y}, \quad\mbox{with } {\overline{V}}_{*}(y):=\liminf_{x\to\infty}\frac{{\overline{V}}(xy)}{{\overline{V}}(x)}, y>1. \end{aligned}$$
Let
\(L_{V}=\lim_{y\searrow1}{\overline{V}}_{*}(y)\). From Chapter 2.1 of Bingham et al. [
1], we know that the following assertions are equivalent:
$$(\mathrm{i}) \quad V\in\mathscr{D};\qquad (\mathrm{ii})\quad 0< L_{V}\leq1; \qquad( \mathrm{iii})\quad J^{+}_{V}< \infty. $$
From the definition of the class
\(\mathscr{C}\), it holds that
\(V\in \mathscr{C}\) if and only if
\(L_{V}=1\).
When
\(\{X_{i}, i\geq1\}\) are independent and identically distributed r.v.s, some studies of the precise large deviations of the partial sums
\(S_{n}, n\geq1\), can be found in Cline and Hsing [
2], Heyde [
3,
4], Heyde [
5], Mikosch and Nagaev [
6], Nagaev [
7], Nagaev [
8], Ng et al. [
9] and so on. In Paulauskas and Skučaitė [
10] and Skučaitė [
11], the precise large deviations of a sum of independent but not identically distributed random variables were investigated. This paper considers the dependent r.v.s with different distributions. We investigate the r.v.s with the wide dependence structure, which is introduced in Wang et al. [
12].
Definition 1.1 shows that the wide dependence structure contains the commonly used notions of the negatively upper/lower orthant dependence (see Ebrahimi and Ghosh [
13] and Block et al. [
14]) and the extendedly negatively orthant dependence (see Liu [
15], Chen et al. [
16] and Shen [
17]). Here, we present an example of WOD r.v.s, which is the example of Wang et al. [
12].
The wide dependent structure has been applied to many fields such as risk theory (see, e.g., Liu et al. [
19], Wang et al. [
20], Wang et al. [
12], Mao et al. [
21]), renewal theory (see, e.g., Wang and Cheng [
22], Chen et al. [
23]), complete convergence (Wang and Cheng [
22], Qiu and Chen [
24], Wang et al. [
25], Chen et al. [
23]), precise large deviations (see, e.g., Wang et al. [
26], He et al. [
27]) and some statistic fields (see, e.g., Wang and Hu [
28]).
Wang et al. [
12] gave the following properties of the wide dependent r.v.s.
2 Main results
Now many studies of precise large deviations are focused on the dependent r.v.s. One can refer to Wang et al. [
29], Liu [
30], Tang [
31], Liu [
15], Yang and Wang [
32], Wang et al. [
20] and so on. Among them, Yang and Wang [
32] consider the precise large deviations for extendedly negatively orthant dependent r.v.s, and Wang et al. [
20] investigate the precise large deviations for WUOD and WLOD r.v.s. Their results have used the following assumptions.
For the lower bound of the precise large deviations of the partial sums
\(S_{n}\),
\(n\geq1\), of the WOD r.v.s, when
\(\mu_{i}=0\),
\(i\geq1\), under Assumptions
1 and
3 and some other conditions, Theorem 2 of Wang et al. [
20] obtained a lower bound: for every fixed
\(\gamma>0\),
$$ \liminf_{n\to\infty}{\inf_{x\geq\gamma n}}\frac {P(S_{n}>x)}{\sum_{i=1}^{n} L_{F_{i}}\overline{F_{i}}(x)} \geq1. $$
The following result will still consider the WOD r.v.s
\(X_{i}\) with finite means
\(\mu_{i}\),
\(i\geq1\), and only use Assumption
1 and some other conditions, without using Assumption
3, to obtain a lower bound of the precise large deviations of the partial sums
\(S_{n}\),
\(n\geq1\).
For the upper bound of the precise large deviations of the partial sums
\(S_{n}\),
\(n\geq1\), of the WUOD r.v.s, when
\(\mu_{i}=0\),
\(i\geq1\), under Assumptions
1 and
2 and some other conditions, Theorem 1 of Wang et al. [
20] gave an upper bound: for every fixed
\(\gamma>0\),
$$ \limsup_{n\to\infty}{\sup_{x\geq\gamma n}}\frac {P(S_{n}>x)}{\sum_{i=1}^{n} L_{F_{i}}^{-1}\overline{F_{i}}(x)} \leq 1. $$
In the following result, we will use the following Assumption
4 to replace Assumption
2 and give an upper bound of the precise large deviations of the partial sums
\(S_{n}\),
\(n\geq1\), of the WUOD r.v.s. Assumption
4 is easier to verify than Assumption
2.
Note that if
\(\sup_{i\geq1} \mu_{i} < \infty\) then Assumption
4 is satisfied. Particularly, the identically distributed random variables satisfy Assumption
4.
If we strengthen Assumption
1 to the following assumption, Assumption
4 can be dropped in Theorem
2.2.
When
\(\{X_{i}, i\geq1\}\) are independent but non-identically distributed r.v.s, then
\(g_{U}(n)=g_{L}(n)\equiv1, n\geq1\), and (
2.1) and (
2.2) hold. By Theorems
2.1 and
2.2, the following two corollaries can be obtained.
When
\(\{X_{i}, i\geq1\}\) and
X are identically distributed r.v.s, Assumptions
1 and
1∗ are satisfied. The following two corollaries can be obtained directly from Theorems
2.1 and
2.3.
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